System and method for stimulating conciousness

ABSTRACT

A simulated consciousness method ( 10 ) for an improved human/computer interface. A computer system ( 12 ) is programmed to have a Digital Life Form ( 32 ) possessing a plurality of attributes ( 65 ). A plurality of actions ( 64 ) taken relative to objects ( 60 ) in the environment ( 30 ) contribute to simulated feelings ( 76 ) which ultimately control the viability of the Digital Life Form ( 32 ). When there are not sufficient energy packets ( 66 ) to sustain the Digital Life Form ( 32 ) then simulated death  52  results. Therefore, only actions ( 64 ) which contribute to the viability of the Digital Life Form ( 32 ) are repeated in the long-run. Some of those actions ( 64 ) include perception of reality, concept formation, and natural language processing.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/380,474, filed Feb. 27, 2009 by the same inventor (now U.S. Pat. No.7,849,026 issued Dec. 7, 2010), which is a continuation of U.S. patentapplication Ser. No. 11/294,622, filed on Dec. 5, 2005 by the sameinventor (now U.S. Pat. No. 7,499,893 issued Mar. 3, 2009), which is acontinuation-in-part of U.S. patent application Ser. No. 09/802,505,filed Mar. 8, 2001 now abandoned by the same inventor, all of which areincorporated herein by reference in their entireties.

COPYRIGHT NOTICE

A portion of the disclosure of the patent document contains materialwhich is subject to copyright protection. The owner has no objection tothe facsimile reproduction by any one of the patent disclosure, as itappears in the Patent and Trademark Office patent files of records ofany country, but otherwise reserves all rights whatsoever.

TECHNICAL FIELD

The present invention relates to the field of software for computers andrelated devices, and more particularly to a method for causing acomputer or other such device to interact with human beings as thoughthe device has human like consciousness. The predominant current usageof the present inventive method for simulating consciousness is in theimprovement of communication in human/machine interaction.

BACKGROUND ART

It is known in the art to cause a computer to emulate certain functionsthat are traditionally associated with human behavior. For example,efforts at artificial intelligence (“AI”) generally attempt to provideknowledge in response to inquiries. However, known AI systems merelyrespond with information that has been programmed into them. That is, amachine programmed with an AI program merely responds in the manner inwhich its human programmers provided for when the program was written.

Experiments in the field of artificial life (“AL”) attempt to cause amachine to function or respond to external stimuli in a manner generallyassociated with a living organism. While such experiments are providinga foundation for future useful devices, the machine/human interface isnot much enhanced by the present state of the art in this field.

It is recognized in the field that it would be valuable to have acomputer which does not respond in some preprogrammed automatic manner.Genetic algorithms have been devised which attempt to get around thisproblem by emulating or recapitulating evolution, in the hope thateventually intelligence will emerge. Neural networks have attempted todo something similar by emulating the function of neurons in higher lifeforms. While it is possible that these methods might eventually help tosolve some aspect of the problem, there has not yet been any usefulbenefit derived from such experiments.

It would be beneficial to have a machine/human interface whichapproaches the flexibility of a human/human interface. However, allknown efforts in the field have been limited to either providing aparticular preprogrammed response to an inquiry, or else have notprovided a useful interface between a user and the machine.

DISCLOSURE OF INVENTION

Accordingly, it is an object of the present invention to provide amachine/human interface which reacts to stimuli in a manner generallyassociated with an animal or a human being.

It is still another object of the present invention to provide a machinewhich simulates consciousness.

It is yet another object of the present invention to provide a computerprogram which will cause a computer to develop a simulatedconsciousness.

It is still another object of the present invention to provide a methodand apparatus for interfacing with a human being as though saidapparatus possesses consciousness.

It is yet another object of the present invention to provide a methodand apparatus for causing a machine to appear to possess consciousness.

It is still another object of the present invention to provide a methodand apparatus for improving a computer/user interface.

It is yet another object of the present invention to provide an improvedcomputer/user interface.

Briefly, a known embodiment of the present invention is a computerprogram which establishes goal directed behavior. A computer isprogrammed to define actions which can either increase or decreasesimulated happiness scores and which can result either in the continuedexistence of a simulated life form or else the demise thereof. Onlyactions which tend to perpetuate the simulated life will be repeated inthe long run. In this manner, a Digital Life Form will be goal directedand will, therefore, act in a manner much as though it is alive and hasactual consciousness. The Digital Life Form can exist entirely within acomputer program for simulation purposes, or can be tied to the “realworld” using sensors, and the like, for practical applications.

The Digital Life Form, thereby, acts as a teleological agent. Anadvantage of the complexity of teleological agents is that they can findways to do tasks for which they were not programmed.

According to the present invention, simulated consciousness is a seriesof discrete causal steps performed by program methods that repeat orcycle operations which a programmer turns into a process by putting theminto a loop internal to the Digital Life Form, in order to simulate itslife and consciousness. The program continuously cycles through theseseveral program methods, thus effecting the simulation. It is animportant aspect of the invention that while some of the behaviors ofthe Digital Life Form are preprogrammed, others are emergent behaviors.That is, the behaviors emerge from the interaction of the Digital LifeForm with its environment and its own previous actions. Emergentbehaviors are not necessarily predictable from the program code becausethe environment is not necessarily predictable. The process steps tosimulate consciousness run in a subsystem layer above those of theDigital Life Form's simulated life processes, and the program methodsthat implement them are to cause the Digital Life Form to perceive itsenvironment, evaluate objects therein, select an action, act, and recordthe action and results thereof to memory. Such action is repeated adinfinitum so long as the Digital Life Form remains “alive ” and, as withbiological life forms, the action may follow any of a variety of pathsbecause the circumstances in the Digital Life Form's environment are notnecessarily predictable. The result is a very realistic simulation.

An advantage of the present invention is that a machine can interfacewith a human being in a manner generally associated with a human tohuman interaction.

A further advantage of the present invention is that it is easier for ahuman to interface with and use a computer.

Yet another advantage of the present invention is that a computer can becaused to develop a simulated consciousness with only a minimal amountof programming.

Still another advantage of the present invention is that it will beeasier and more natural to use a computer or computerized machine.

Yet another advantage of the present invention is that it will bereadily implemented using available computer hardware and input/outputdevices.

These and other objects and advantages of the present invention willbecome clear to those skilled in the art in view of the description ofmodes of carrying out the invention, and the industrial applicabilitythereof, as described herein and as illustrated in the several figuresof the drawing. The objects and advantages listed are not an exhaustivelist of all possible advantages of the invention. Moreover, it will bepossible to practice the invention even where one or more of theintended objects and/or advantages might be absent or not required inthe application.

Further, those skilled in the art will recognize that variousembodiments of the present invention may achieve one or more, but notnecessarily all, of the above described objects and advantages.Accordingly, the listed advantages are not essential elements of thepresent invention, and should not be construed as limitations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram depicting an embodiment of a simulatedawareness method, according to the present invention;

FIG. 2 is a diagrammatic view of a general purpose computer system suchas may be used for practicing the present inventive method;

FIG. 3 is a simulated environment, including a digital life form,according to the presently described embodiment of the invention;

FIG. 4 is a flow diagram depicted a somewhat more complicated simulatedconsciousness method;

FIG. 5 is a flow chart depicting a simulated feeling, as shown in FIG.4;

FIG. 6 is a flow diagram depicting an example of a method for creating asimulated consciousness;

FIG. 7 is a diagrammatic representation of a hierarchical processaccording to the present invention; and

FIG. 8 is a diagrammatic representation of a concept chain such as mightbe formed according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

While this invention is described in terms of modes for achieving thisinvention's objectives, it will be appreciated by those skilled in theart that variations may be accomplished in view of these teachingswithout deviating from the spirit or scope of the present invention. Forexample, the present invention may be implemented using any combinationof computer programming software, firmware or hardware. As a preparatorystep to practicing the invention or constructing an apparatus accordingto the invention, the computer programming code (whether software orfirmware) according to the invention will typically be stored in one ormore machine readable storage devices such as fixed (hard) drives,diskettes, optical disks, magnetic tape, semiconductor memories such asROMs, PROMs, etc., thereby making an article of manufacture inaccordance with the invention. The article of manufacture containing thecomputer programming code is used by either executing the code directlyfrom the storage device, by copying the code from the storage deviceinto another storage device such as a hard disk, RAM, etc. or bytransmitting the code on a network for remote execution. The method formof the invention may be practiced by combining one or more machinereadable storage devices containing the code according to the presentinvention with appropriate standard computer hardware to execute thecode contained therein. An apparatus for practicing the invention couldbe one or more computers and storage systems containing or havingnetwork access to computer program(s) coded in accordance with theinvention.

A presently known mode for carrying out the invention is a computerprogram, operative on a general purpose computer, for accomplishing theinventive method as described herein. An example of an inventivesimulated awareness method is depicted in a flow diagram in FIG. 1 andis designated therein by the general reference character 10. FIG. 2 is ablock diagram of a computer system 12 such as is anticipated to be usedto accomplish the simulated consciousness method 10. Illustrated is ageneral purpose computer 14, having the usual appendages such as akeyboard 16, a pointing device 18 (generally a mouse), a display screen20, a printer 21, a removable medium 22 (such as a floppy disk or CDROM) in a removable medium drive 24, and a fixed medium drive 26. Theinventive simulated awareness method 10 will generally be stored uponthe removable medium 22 for downloading into the fixed medium drive 26of the computer system 12. In addition, a data base 28 consisting ofdata to be used with the present inventive method will generally bestored on the fixed medium 26.

According to the present inventive method, goal directed behavior isused to simulate the sort of response usually associated with aconscious being. A primary goal is the “survival” of a digital life form(“DLF”). A diagrammatic representation of a simulated environment 30including a DLF 32 is depicted in the view of FIG. 3 and will bediscussed in greater detail, hereinafter.

In the presently described example of the invention, the “life” of theDLF 32 is represented numerically in the computer system 12. This simpleconcept will be familiar to those practiced in the art of computergames, wherein a numerical score is used to represent the relativevitality of a character. However, an essential difference here is thatthe vitality of the DLF is maintained by the actions of the DLF itself,and as such it is a conditional entity.

Referring again to FIG. 1, the simulated awareness method 10 functionsas an endless loop (with exceptions as discussed hereinafter) wherein anaction 40 attempts to achieve a goal 42 which, if successful, asdetermined in a success decision operation 44, will result in thesurvival 46 of the DLF 32 (FIG. 3). Subsequently, another action 40 isselected in a select action operation 48, and an experience tally 50 isincremented. These operations will be discussed in more detailhereinafter. As can be seen in the view of FIG. 1, should the action 40not be successful (or alternatively, should successive actions not besuccessful, as will be discussed hereinafter), then the DLF 32 isdeactivated, simulating the “death” 52 of the DLF 32, as consistent withits conditional nature.

The present inventive DLF 32, as with any life form, must cause its ownfuture existence (survival) precisely because it is a goal directed orinternally driven entity, as opposed to a rock, which is not goaldirected. Any actions of a rock are simply the result of outside forces.Failure to maintain goals causes the life form to cease to exist, acondition in which it is no longer part of reality and one that isirreversible. Only behaviors that are successful will be repeated in thelong run, as will be discussed in more detail, hereinafter.

Prior to the present invention, there has been a profound andfundamental difference between state of the art computer systems andbiological life forms, between mechanical/logical systems andteleological systems. If something is a real life form, that is, if itis alive, it must be conditional, because that is the essentialattribute of all life forms. The artificial life form (DLF 32) shouldtherefore be goal directed, which means that it be internally driven byits own values, energy source, internal locus of control, and the valuesignificance to itself of its own values (what it needs to stay alive).In order to act according to the present inventive method, the DLF 32should have values (or an equivalent thereof) and act to gain and keepthem on its own power. Simulated death is the primary means thisinvention uses to solve the problem of the apparent need to predefine asimulated life form's future actions. Simulated death solves thisproblem because only pro-life actions get repeated in the long run. DLFs32 that fail in pro-life actions or attempt anti-life actions cease toexist and therefore can no longer act. Thus they have no long termcausal significance in the simulation.

Referring again to FIG. 3, it can be seen that in the simulatedenvironment 30 the DLF 32 exists along with a plurality of externalobjects 60. These objects could represent things such as food 62, whichwould contribute to the viability of the DLF 32. Another example wouldbe that an object 63 could represent a threat to the DLF 32 if the DLF32 does not take action to avoid it.

According to the present inventive method, a DLF 32, just like a livingorganism, must take in materials and energy from the environment 30, andit must use the appropriate materials and energy for self maintenance,self repair and self reproduction. And, also like a living organism,once the DLF 32 has died, it cannot be reconstituted—failure isirreversible. In order for a DLF to appear to have consciousness, itsprimary purpose cannot be to achieve human goals, which is howconditional programming structures are used in all state of the artcomputer programs, but the goals of the DLFs 32 themselves.

This means that DLFs 32 must be logically structured to take action tomaintain their existence, and that they must be deleted if theirsurvival actions fail.

Accordingly, the DLFs 32 must be equipped with a pallet of potentialactions 64 through which it can interact with the objects 60 in itsenvironment 30. Human programmers can predefine basic actions such aslook, find, or eat, to build a starter simulation system goal directedaction refers to actions (or sequences of basic actions) selected by alife form for survival purposes.

Still referring to the view of FIG. 3, it can be seen that the DLF 32has several attributes 65, examples of which are shown in FIG. 3. In aslightly more sophisticated example, the DLF 32 might possess simulated“feelings”. An example of attributes 65 for a DLF possessing simulatedfeelings might be as follows:

-   -   1. Name:006023    -   2.Age: 84    -   3. Starting Energy Packets (“EPs”): 100    -   4. Current EPs: 350    -   5.Current percepts: P1, P2, . . . Pn    -   6.Actions Available: Look, Find Food, Eat, Stop    -   7. Simulated Feelings:        -   a. Hunger/Fullness: −2        -   b. Interest/Boredom: +3        -   c. Company/Loneliness: +2        -   d. Clarity/Confusion: +5        -   e. Activity/Laziness: −1        -   f. Confidence/Fear: +2        -   g. Happiness: 1.5

A programmer skilled in object oriented programming can make simulatedfeelings attributes of a class of DLF 32 program objects. The simulatedfeelings give the DLF 32 an instantaneous indication of its life status,and, if put into a window on the computer screen as part of a DLF 32program interface, a human observer can see the same status. By beingconscious of its own life status, a DLF 32 can take actions to cause itsfuture survival, since it would have the information that is aprerequisite to such actions. Simulated feelings are the simplest formof simulated self awareness or self consciousness, though at this levela DLF 32 is not aware that it is aware of itself.

As can be seen from the example above and that of FIG. 3, a DLF 32 canhave attributes 65 such as a quantity of energy packets (“EPs”) 66 whichrepresent its degree of vitality. When a DLF 32 reaches zero EPs 66, itslife would end. Therefore, maintaining an adequate energy supply(sufficient EPs 66) becomes the basis for all other actions a DLF 32 maybe capable of performing. Therefore, once the DLF 32 programming objecthas been created and defined, processes called methods (object orientedcomputer programming code) must be defined to enable the DLF 32 to takeaction and an action selection method to enable internal control of itsactions to find simulated food in its simulated environment to generatemore EPs 66. This must be a continuous process to enable the DLF 32 tosurvive, just like a biological life form. These methods define theactions 64 depicted in the view of FIG. 3.

As seen in the view of FIG. 3, the DLF 32 can include one or morepercepts 67. As defined herein, a percept 67 is a list of the perceivedcharacteristics of the objects 60 that is calculated from input sensedby the DLF 32 from the objects 60 in its environment 30. Each percept 67is a list of the properties and values (property measurements) of acorresponding object 60. To the DLF 32, the percepts 67 are theidentities of the objects 60. Therefore, the percepts 67 are theprocessing units of simulated perceptual consciousness in a DLF 32, aswill be discussed in more detail hereinafter.

As with the DLF 32 program object itself, the program objects 60 in theDLF's 32 simulated environment 30 must be created and defined (to saveresources and make the system simpler during initial development), butsince these objects 60 are non-conditional (non-living), most need fewaction methods for simple reality simulations. More complex andsophisticated simulated environments (not shown) in which non-livingobjects are animated (or contain other DLFs 32), would however, requirecoding extensive action methods for those objects. For this reason, atsome point in the development of the system, it will become desirable touse objects sensed in the real world as with TV cameras, microphones,pressure sensors, and the like, to eliminate the need for such extraprogram coding and put the DLFs to practical real world use.

By way of example, the program code for an “Eat” method 69 canautomatically include digestion, generating energy EPs 66, and thesimulated feeling of being “full”. The code for a “Stop” method, in thisexample, is a simple loop that continuously tests for feeling offullness, and stops the Eat method when that condition is met. The codefor the Death 52 method erases the current DLF 32 from the computer'smemory and calls the Birth method which increments the DLF 32 nameattribute by one and resets the other attributes to initial conditions.

It will be advantageous to save pro-life behaviors and maintain thembetween generations of the DLFs. This may be done by not erasing thebehaviors from memory at simulated death 52, thereby simulating geneticevolution to carry the behaviors forward to the next generation of DLFs32. Alternatively, some other method not yet contemplated by theinventor might be used for this purpose. In any event, it is importantthat the only actions that get repeated long term are the valuableactions. Life forms (DLFs 32) that repeat any other kind of actionssimply get wiped out and no longer exist, and only actions of those DLFs32 that are relatively successful should be carried forward tosubsequent generations.

The complexity of the program code for sensing the environment 30 willdiffer greatly depending on whether the environment for a DLF 32 issimulated or real. The two types of environment are essentiallyequivalent, except that real sensors sensing reality provide much moreaccurate and detailed real time data of the world, whereas simulatedworlds are limited to human imagination and computing resources.Simulated environments are primarily useful for developing, testing, andproving program methods while conserving resources. Sophisticatedsimulations intended for practical uses will need to interact with thereal world to be effective.

An example of a slightly more complex simulated consciousness method 10a is depicted in the view of FIG. 4. In a perceive environment operation70 objects 62 in the environment 30 are located and then identified. Inthis simple example, the only objects 60 of interest are food 62. Iffood is not found, a check is made to determine if there are sufficientEPs 66 to maintain existence. If not, the death 52 operation is called,wherein the DLF 32 is deleted from memory and a birth operation 72 iscalled to create a new DLF 32. If there are EPs 66 to continue, the loopreturns to the perceive environment operation 70. When food 62 isidentified, the program proceeds to an eat operation 74 wherein the food62 is assimilated and used to create EPs 66. This process is continueduntil there is no more food 62 immediately available or else until theDLF 32 is “full”—that is, until it has achieved its maximum quantity ofEPs 66.

As can be appreciated in light of the above discussion and the flowdiagram of FIG. 4, once various objects 60 have been perceived by a DLF32, they must be evaluated with the DLF's 32 life as the standard ofvalue. To a biological life form, since its continued existence isconditional, every percept is either a value or a disvalue relative toits life. That is, every percept has value significance to the life formas being information about its world that is either for or against itslife. In order for a DLF 32 to be an accurate simulation of a life form,therefore, a DLF 32 should also be able to determine the valuesignificance of its percepts 67 (FIG. 3). One way to accomplish this isto simulate pleasure and/or pain, in much the same way the othergenerally biological functions have been simulated as discussedpreviously herein. For example, in FIG. 4, a “feeling” operation 76calculates whether or not the DLF 32 is experiencing the feeling ofbeing “full”. In like manner other feelings can be simulated.

The pleasure/pain systems of biological life forms are automatic, builtin value systems. In general, things that are good for a life form causeit to feel pleasure, and things that are bad for it cause it pain(either physical, emotional, or both). In order to create a digitalsimulation of a life form a similar automatic, built in evaluationsystem is desirable and, like actions, this can be copied frombiological life forms and predefined so evolution does not have to berecapitulated by DLFs 32. Since computers are not biological, simulatedpleasure and pain must be calculated based on simulated values whichserve as standards with the life of a DLF 32 being the ultimatestandard. The ideal is to make simulated evaluations as causally andfunctionally equivalent to the biological ones as is technicallypossible. An example of a flow chart for calculating a simulated feelingis depicted in the view of FIG. 5. For example, to calculate if the DLF32 feels “full” in computational terms, a method is written thatcompares the number of EPs 66 that a DLF 32 has with the range that itssimulated life requires. Having EPs 66 is a value to a DLF 32's life;without them the DLF 32 will die just as a biological life form will diewithout food. A simulated feeling 76 can be calculated for any number ofEPs 66 a DLF 32 has at any specific time by comparing the number itactually has to its required range. As can be seen in the view of FIG.5, in this example, the feeling 76 is calculated by a getting currentEPs operation 80, then a comparing value operation 82 wherein thecurrent EPs 66 are compared to a set range of acceptable values, thenthe feeling 76 is calculated in a calculate feeling operation 84, basedupon where the current EP 66 quantity lies within this spectrum.Finally, the calculated feeling 76 is stored as an attribute of the DLF32 in a store attribute operation 86. The attributes of the DLF 32 arediscussed above and in relation to FIG. 3.

Early in a DLF 32's life, when there are few examples of percepts 67 andhow the DLF 32's previous actions changed them, most of the DLF 32'sactions will be selected by trial and error. However after an extendedlife and, perhaps, many thousands of perception/action events, theaction selection methods will have much more data to use and will,therefore, be able to select actions with the greatest survival valuemore efficiently. The operating principle here is that identity(processed data in the “memory” of a DLF 32) determines action capacity.As the amount of data increases in the DLF the identity of the DLFeffectively changes in a way that increases its action capacity, just asoccurs in biological life forms to varying degrees. Some examples ofaction strategies that might be provided by a programmer are as follows:

Continue the last action: This is a useful strategy when an action issucceeding in improving simulated feelings (such as eating to reducehunger).

Select the action that resulted in pleasure in the past when a givenobject was perceived: This option is similar to the previous one, but isrecalled from a memory association from the past.

Select no action: This is a useful option when all simulated feelingsare positive and no action is required to change them. It is also anexample of an optional action. Follow a pre-programmed process (when agiven object is perceived, as with instinctual behavior in biologicallife forms such as nest building, or habits in humans): This option is agood strategy for a goal requiring complex actions or series of actions.

Random action selection: This option is analogous to trial and erroractions observed in biological life forms and is useful for newsituations when no other action gets selected. It is another example ofan optional action.

According to the present invention, actions are not preselected, butrather are selected by simulating the perceptual consciousness processand, as with its biological counter-part, this process is an automaticone (in the teleological sense). There is no other basis for makingselections because options are limited at the perceptual level ofawareness to the objects in the DLF 32's world and the DLF 32's ownsimulated pleasure-pain responses to those objects. However, actionselection is teleological because its goal is a DLF 32's survival, theDLF 32's simulated life is the standard of value and it, therefore,cannot be explained as simple mechanistic, billiard ball type ofcausality.

When creating action selection methods the following points should beconsidered. An action selection method should insure that some action isalways selected for any perceptual event (even a “no action” method isan action in this context). An action selection method should beteleological in that its goal is causing the survival of the DLF 32 withits simulated life as the standard value, and does so by increasing theDLF's 32 simulated happiness. Only survival actions get repeated in thelong run. “No_Act” and/or “Random_Act” methods can allow for a DLF 32 tomaintain its simulated happiness for a time, provide for trial and erroractions, and allow for the unexpected or the novel event to besimulated.

It will be recognized by one skilled in the art that after a great many“experiences” by the DLF there will accumulate a great deal of data.Therefore, it may be desirable to divide the data base 28 (FIG. 2) tohave both short and long term storage wherein much of duplicate shortterm information is deleted

FIG. 6 is a flow diagram depicting an example of a process 90 forcreating a simulated consciousness method 10. In the example of FIG. 6,it can be seen that in a define DLF operation 92 the attributes 65 for aDLF 32 are determined and defined, and provision is made to store suchin the data base 28 of the computer 14. Again, one skilled in the art ofobject oriented programming will appreciate that this is a relativelysimple process. In a provide access to environment operation 94provision is made for allowing the DLF 32 to perceive its environment30. As discussed previously, herein, the nature of this operation willvary according to the nature and complexity of the environment 30. Ifthe environment 30 is entirely simulated, as in the simple example ofFIG. 3, then the programmer can merely define the objects 60 in theenvironment 30 as program objects. Alternatively, if the DLF 32 were tobe intended to operate in the “real world” then sensors could beprovided to sense real world objects (not shown) and identify them. Thetechnology for this currently exists and is being further developed, andis not an aspect of this particular invention.

In a provide selection of actions operation 96, a programmer will defineselected actions 64, as previously discussed herein, and will furtherdefine the circumstances under which particular actions 64 will beselected. In a define consequences operation 98, the programmer willprovide for the simulated feelings 76 which will assist in determiningthe appropriate action 64. Also, as previously discussed herein, theconsequence of simulated death 52 and birth will be programmed.

FIG. 7 is a diagrammatic representation of a hierarchical process 100such as will enable a DLF 32 to achieve simulated consciousness. As canbe seen in the view of FIG. 1, the DLF 32 will first form percepts 67 ina percept formation operation 102 such as has been discussed previouslyherein. It should be noted that many percepts 67 will be created,essentially one for each object 60 or entity encountered in theenvironment 30 of the DLF 32, and these percepts 67 are the identity ofthat object 60 (properties and measurements). Therefore, the diagram ofFIG. 7 is not a flow diagram, but rather a hierarchical diagram showingthe levels of operation of the DLF. As can be appreciated by one skilledin the art the percept formation operation 102 will be repeated, asnecessary, as objects 60 are encountered in the environment 30.

FIG. 8 is an example of a concept chain 104, which will be discussedhereinafter in relation to the remainder of FIG. 7. When the DLF 32 hasstored sufficient percepts 67 to make comparisons, a concept 106 can beformed by such comparison. For example, any shapes which are closed, andcomprised of three straight sides and three corners can be groupedtogether to form a concept 106 “triangle”. When sufficient concepts 106have been formed for comparison, these can be compared to makeadditional concepts 106. In the example of FIG. 8 it can be seen thatthe concepts 106 “triangle”, “circle” and “square” have similarcharacteristics which can be grouped under the concept 106 “closedshape”. In like manner, the entire concept chain 104 of FIG. 8 can beformed, given sufficient experience by the DLF 32. Higher and lowerlevel (more abstract) concepts 106 are formed by comparing theattributes of other concepts 106, as can be seen in the view of FIG. 8.This means both more general and more specific concepts 106 can beformed at any point in the mid levels of the hierarchy of concepts.Concepts 106 can therefore identify any kind of relationship betweenpercepts 67, and at all levels of complexity, but they all must beconnected by unbroken chains to perceived objects in the DLF's 32 worldat some point.

It should be noted that concepts 106 start being formed by comparison ofcertain particular attributes of percepts 67. For example, looking onlyat the relative position of objects 60 can lead to the formation ofconcepts such as “above”, “to the right of” and the like. Likewise,concepts can be formed relating to intangibles. That is, concepts arecalculated for objects, actions, relationships and even for otherconcepts.

Referring again to FIG. 7, it can now be appreciated that a next levelof operation of the DLF 32 following percept formation 102 will beconcept formation 108, wherein concepts 106 are formed, as discussedabove. One skilled in the art will recognize that concept formation 108will not be an inherent characteristic of a DLF 32, but rather will beprovided for as one of the actions 40 available to the DLF (much like“eat”, or the like), which have been discussed previously herein and mayrequire multiple simulated conscious loop cycles to complete.

It should be noted that the formation of concepts 106 does notinherently provide for a name for the concepts such as have been used todiscuss the example of FIG. 8. That is, just because the DLF 32recognizes the similarities between objects such that it can group alltriangular shaped objects together by such similar characteristics, thatdoes not mean that the DLF will understand that these are called“triangles” in English or by some other name in other languages. Theconcept 106 without a word associated (its name) may be referred to asan “implicit concept”, wherein the DLF 32 has the data to form aconcept, but does not yet have a name for it. It is a real, workabledata structure in the system, but not yet linked by association to theDLF 32's symbol system. As discussed above, concept formation is a formof simulated volitional (free-will, or optional) behavior. Percepts 67are calculated automatically (in a teleological, not mechanistic sense).Concepts 106, however, are calculated only as optional behavior, thisbeing non-automatic action. (Optional behavior consists of actions a DLF32 can perform if and only if its necessary actions such as eating havebeen completed successfully, thus ensuring it has sufficient EPs 66 tostay alive. This is necessary because there are an unlimited number ofpotential concepts 106, and a DLF 32 could actually die by “thinking”too much.) But what the computer 12 cannot do on its own is to come upwith a real language word. The computer 12 could come up with its ownword, but then it would have to be translated in order for the computer12 to communicate with people in the real world. In order to providereal English word, a human tutor should interact with the DLF much likea child would learn. The ability to decode and encode sentences dependson both words and concepts, because the chains of concepts 106connecting them to percepts 67 is the meaning of the words. The DLF 32will perceive words as objects 60 and can form concepts 106 of bothobjects 60 and their relationships, as well as sentences and the partsof sentences. (The sentences themselves are just another form ofperceptual object 60 in this system.) This process is representeddiagrammatically in a word association operation 110 in FIG. 7, whereina concept 106 is associated with a word, as discussed above. Onceconcepts 106 are formed, as shown in FIG. 7, the encoding of a sentencemay follow. This is a process that starts with objects 60 and connectsthem to the words that make up the sentence. Reversing the arrows wouldbe the decoding of a sentence, essentially by reconnecting the words inthe sentence to objects 60 in reality. Both processes operate by tracingpreviously calculated conceptual chains 112, or in some cases, bycalculating new ones. In the view of FIG. 8 it can be seen that each ofthe concepts 106 is represented by a natural language word 112 in theconcept chain 112. Simulated perception, concept formation, and theprocesses of encoding and decoding sentences, taken together asdescribed herein, solve the problem known in the state of the art asnatural language understanding and production. It should be noted that,as discussed above, the DLF 32 can form concepts on its own withouthuman intervention as one of its optional actions 40. The human contactis only required to enable the use of natural languages, as described.

Various modifications may be made to the invention without altering itsvalue or scope. For example, alternative or additional actions, methods,and the like might be used instead of or combined with those describedherein. One additional action method could be the ability to compare awider variety of characteristics of the objects 60 a DLF 32 perceives,to make the DLF 32 better able to group and abstract percepts bysimilarity. Another example of an obvious modification would be toincorporate the invention into a robotic device or other such machinethat better simulates human sensors, brain, and the ability to identifyobjects in the real world, instead of the general purpose computer usedin the example of this disclosure.

All of the above are only some of the examples of available embodimentsof the present invention. Those skilled in the art will readily observethat numerous other modifications and alterations may be made withoutdeparting from the spirit and scope of the invention. Accordingly, thedisclosure herein is not intended as limiting and the appended claimsare to be interpreted as encompassing the entire scope of the invention.

INDUSTRIAL APPLICABILITY

The inventive simulated awareness methods 10 and 10 a are intended to beused in ever increasingly complex forms to eventually result in a DLF 32which can interact with humans and the “real” world in which we live,thereby resulting in a program which appears to have consciousness andwhich can solve problems for which it is not specifically programmed.

A relatively short development time is provided for, since thisinvention copies many design ideas from real life forms, instead ofattempting to re-evolve them to recapitulate evolution in some manner,such as is attempted by genetic algorithms, and the like. In otherwords, just as the AL researchers did not re-evolve the gait of insectrobots, but rather reverse engineered their operation by copying reallife forms, so this invention seeks to reverse engineer the simulationof goal directed behavior and consciousness rather than re-evolve it. Akey is to identify the essential elements and program substitutions.This is the pre-defined part of the simulation system. This aspect ofthe design involves identifying the necessary and sufficient set ofelements to develop the substitutions for, and then writing the softwarecode for those elements. This self defining stage of the development ofthe simulation system is the management and tutoring of those basicelements as they simulate the active processes of life andconsciousness.

The inventive method can be practiced using a reasonably powerfuldesktop computer with at least 64 MB of memory, a 1 GB hard disk drive,and an object oriented programming environment to write a goal directedprogram that simulates a life form. Writing a program to simulate goaldirected behavior on the computer system 12 amounts to creating the DLF32 and the simulated environment 30 in which the DLF 32 will live.

Simple simulations involving a few thousands of percepts 67 wouldrequire less computer resources and could be done on a high end PC, butcomplex simulations of higher life forms that involve millions ofpercepts 67 for natural language understanding could require a morepowerful computer system, such as those used for large Internet servers.

A simulated or virtual environment can be made very sophisticated and iseasier and less expensive than using a real one, because it can existentirely in a computer's memory, so no external sensors or actuators areneeded. To simulate high order functions such as rational consciousnessaccurately, a DLF 32 will eventually have to interact with the sameworld that human beings do, including interaction with people. However,simulations of simpler DLFs 32 do not require real world contact. Bothsimple and complex simulations that use external robot technologies arepossible with today's technology, and will become even more realistic inthe technical improvements that will come in the near future.

The present invention is based, in part, on the concept that knowledge,in order to be objective, has to be connected to reality (what isperceived). Every object has an identity which is unique, objectsinteract with one another. This is referred to as causality. Causalityis not merely one event following another. Rather, interaction of theidentities of objects is causality. There are essentially two types ofobjects—non living and living. Non living objects are totally externallydriven. They exist unconditionally, whereas living objects existconditionally. Certain actions they must take or they die and cease toexist. This makes them a different kind of entity, with different kindof causality. A DLF 32, according to the present invention, like a reallife form, can have optional behavior. Once the DLF 32 has satisfiedsurvival needs, it is free to do what it wants. It can engage in moresurvival action, can do nothing, do random actions, or the like.Alternatively, like human beings, the DLF 32 can form concepts 106—itcan look around the world and learn. In both DLFs 32 and humans thereare two types of behavior. The first of which is necessitated forsurvival which, even though “automatic” in a goal directed, biologicalsense, is different from that of mechanistic automatons, because it isteleological. To be biological is to be internally powered andregulated.

Since the life form, simulated or real, must maintain its survival byinternal self regulation, it must take such necessitated actions and, inorder to do so it must be able to see (or sense) its environment toidentify objects that exist there and predict likely outcomes of itsactions (set goals). There will be a survival advantage in taking rawdata and integrating it into percepts 67 and then into concepts 106,since the DLF 32 will be able to learn from its experience thereby, andsince concepts will allow the DLF 32 to act based on generalities, andthe like, thereby reducing the number of calculations required becausepercepts and concepts are stable, invariant condensations of what issensed in a highly dynamic outside world. As discussed previouslyherein, concept formation is among the second, optional, types ofbehavior which, while not immediately necessary for survival, might wellenhance the likelihood of survival of the DLF 32 in the long run (as itdoes in fact for humans). Because such optional behaviors must beplanned for the DLF 32 by the programmer, they will be limited inquantity as compared to a nearly infinite variety of possible (andemergent) optional behaviors that will be possible once the DLF 32 hasformed a multitude of concepts 106. However, as discussed herein, it isimportant that the DLF does have at least some such optional behaviorsfrom the start.

It should be noted that the DLFs differ significantly from what manyseem to consider to be “life forms”. That is, it would not be correct tolump real life-forms, computers, life-forms simulated primarily toattain human goals, and life-forms simulated to primarily attain theirown goals all into the same category. Although this present inventionmay “look” like a state of the art simulation of life, such as isrepresented by Heleno et al, “Artifical Animals in Virtual Ecosystems”,published in Computer Networks and ISDN Systems, Volume 30, Issues20-21, November 1998, pages 1923-1932, such a comparison would not bevalid due to significant differences. The same is true for senseperception (which is often incorrectly lumped in with sensations andbitmaps), and for concept formation (which nearly everyone is taught issimply a case of either intuition or of making up a definition base on arational that can be sold to enough other people to get accepted).Concept formation is almost never taught as a quasi-mathematical methodthat is based on direct observation of reality as it is used in thisinvention.

Computer systems, including so-called “autonomous agents” aremechanistic, whereas life-forms are teleological, or goal directed inthe sense of being self-regulating. The system described by Heleno etal., for example, is a study and educational tool created to provide“dynamic pictorial information” to people who then find it is “easilyinterpreted by a human being and clarifies the behavior of theinteractions.” The so-called “goals” of the animals in the model aremerely variables that make it easier for scientists to have a“reproduction of natural phenomena” in their labs. They are an artifactof a subject being studied. They are not the “goals” of the actuallife-forms to be used for the life-forms' own survival purposes. If thesystem described by Heleno et al. were used to study the behavior ofsmall fires spreading and joining into an inferno, the subject of goalswould not be involved, for fires are not life-forms. Yet it would workall the same, because Heleno et al. makes no distinction betweenmechanism and teleology. That is, simulations such as described byHeleno et al. assume that life is inherently mechanistic. Similarly,simulations designed to display behavior, such as the popular“artificial pet” simulations, while useful for their own purposes, arenot comparable to the present invention. The inventive DLF system ismuch broader and more complex because it can simulate life forms ingeneral. In addition, such machine simulations are a “machine” bydefinition, which means that they are non-teleological by definition,and are therefore incapable of goal-directed action for its own sake.Moreover, in such simulations the relationship of the machine to a humanbeing is one of being a “user.” By inference, therefore, an “electronicpet machine” or the like, has no goals that it acts to achieve for itsown sake, but like the animals in the Heleno et al. system, they aremerely a tool to help its human “user” achieve human goals. Bycomparison, the inventive DLF system will be used by humans to helpachieve human goals in the same sense that real animals such as horsesare, because DLFs (like real horses) are teleological, DLFs willprimarily act to achieve their own goals and only secondarily act tohelp achieve human goals. This point also applies to the question ofsimulated feelings. These simulated feelings do not exist for the sakeof the pet machine itself, they exist only for the sake of their effecton the human user. Whereas, the simulated feelings of pleasure and painin the DFL system function primarily as warning indicators for the lifestatus of the DLFs themselves, and only secondarily as for human users.

The distinction between the mechanistic and teleological action is notmere semantics. It has been studied and argued extensively in theliterature. Non-living objects exist unconditionally, they function bysimple mechanistic or “billiard ball” causality, and they do not act tomaintain their existence. For biological life-forms their very existenceis conditional: They must act to remain alive and in existence, in fact,to cause their own future existence. Moreover, they must act in veryspecific ways. A more complex causal explanation is required than simplemechanistic causality provides. Therefore, another important differenceis that this present invention simulates the more complex form ofcausality that makes biology possible at all, whereas simulations oflife forms in Heleno et al, and the like do not.

The inventive DLF system rests on the premise that computer technologycannot have the attributes of a life-form, such as conscious behaviors,based on a purely mechanistic design. Artificial Intelligence (AI) andArtificial Life (AL) design strategies are oversimplifications of thereality of life processes because they ignore teleology (goal-directedaction), except insofar as it furthers human goals. Thus the state ofthe art design strategies preclude digital life-forms that live fortheir own sake from the outset. All state of the art systems known tothe inventor exist for the sole purpose of satisfying human goals, notgoals of the simulated life-forms they mimic.

If the purpose of simulating hunger in the inventive DLF system weresimply to create a pet simulation machine it might be comparable to theappearance to humans of a “pet machine”, or the like, taking action tosatisfy hunger or of behaviors that make the pet machine appear full orto have more energy after eating. But that is not the purpose ofsimulating hunger in the inventive DLF system. Rather the purpose is tosimulate an active metabolism in a conditionally existing DLF for theultimate purpose of simulating teleological goal causation. In otherwords, a simulated “pet machine” might eat and later look satisfied andhave more energy because it is scripted to satisfy the observationalneeds of the human user. Alternatively, the inventive DLF eats becauseit is simulating hunger in the manner of a real animal, and is driven byits pain system to do so with the goal of causing its own survival asseen from its own internal perspective.

Similarly, prior art AI systems, and the like, are designed to be aneasy to use modeling and study tool or a pet machine, but not tosimulate the actual teleological causation of biology as is theinventive DLF system. In known prior art systems, all the functions areclearly designed for use by humans, not for the animals or pet machineto identify reality for their own sake, for their own goals, and fromtheir own perspective. The emphasis here is clearly on the ease of useof the system for human users (scientists, students, pet machine owners,etc.), not the value of sense perceptions of the actual animals in thesimulation itself from the animals' own perspective. Whereas, in theinventive DLF system, the whole purpose of having sense perceptions inthe first place is to enable DLFs to gain the identity information aboutobjects in the outside world so that DLFs can survive, because they needto identify reality so that they can take action to cause their ownfuture existence, as opposed to help some scientist write his papers orplay with an artificial pet.

Sense perceptions in AI simulation type machines, as is typical in theknown state of the art, are simply bitmaps and quantities in variables.However, in the inventive DLF system sense perceptions are computed frombitmaps or variables while using them as data, but are not bitmapsthemselves. From the perspective of DLFs, these sense perceptions AREobjects in the world, not quantities in variables. Moreover,consciousness is simulated using this more complex perceptual data type.In the DLF demonstration program embodiments created to date, thesecomputed percepts are simple lists, but as they function in the DLFsystem they are a data type that is new to the art and designedspecifically for this purpose. Other non-list based future designs arepossible (such as more complex perceptual forms of objects produced by aReality Identification Cortex that better simulates the human brain),but the point is that simulated consciousness of DLFs will not be“conscious” of a database of simple variables or bitmaps, they will be“conscious” (from their perspective inside the system) of percepts thatconsist of a foreground of objects against a background of otherobjects, real world objects that have real characteristics or featuresthat make up their identities, such as those that people can and doperceive when they look at trees and rocks and cars and other people orhear words spoken as “sound objects,” not as text strings consisting ofbits. In DLF simulated percepts, each characteristic or feature in thesemulti-feature data objects will consist of an invariant property and avalue measurement that can then be used in later computations,especially those used for forming (calculating) concepts according to aspecific, quasi-mathematical method.

In the state of the art, consciousness is defined as either variouskinds of spiritual mysticism or in science as a transparenteppi-phenomenon with no identity of its own. Ayn Rand was the firstperson to recognize consciousness as a process of awareness when shesaid in her book Introduction to Objectivist Epistemology:“Consciousness IS Identification.” That is, identification done by alife-form from the perspective of the conscious life-form doing theidentifying, not some outside user. By “identification,” she meansidentification of objects, the ordinary things everyone sees in theworld. By process, she means an identify-able series of steps like anyother process that is performed by the brains of certain life-forms. Inaddition, she defines “percepts” as the output of this process and as “agroup of sensations automatically retained and integrated by the brainof a living organism.” (Note: The term “automatic” is used here in thecontext of biological, goal-directed, automatic behavior of life-forms,not the computational idea of mechanistic automatons.) The DLF system isdesigned to animate the process of conscious perception in simulation,as it was conceived by Ayn Rand by substituting the mechanisms of acomputer system for the mechanisms of physics and chemistry that animatelife-forms, with special programming added to simulate goal directedbehavior. In the DLF system, “sensations” are analogous to bitmaps orvariable definitions in prior art systems, but they are simply used as astarting point and as data. There is then additional processing tosimulate the “integration, retention, and identification” parts ofRand's process explanation of how consciousness works in life-forms.This design is very different from the “sense perceptions” in Heleno areintended to model virtual ecosystems and produce: “dynamic pictorialinformation” . . . that provides a “visualization of a naturalenvironment in a VR system.”

Since the simulated consciousness method 10 of the present invention maybe readily produced and integrated with existing computer systems andsensing devices, and the like, and since the advantages as describedherein are provided, it is expected that it will be readily accepted inthe industry. For these and other reasons, it is expected that theutility and industrial applicability of the invention will be bothsignificant in scope and long-lasting in duration.

1. A computer generated entity, comprising: a plurality of attributes,wherein at least one such attribute defines the vitality of the entity;and a plurality of actions, at least one of which will affect thevitality of the entity; and wherein said actions simulate actions by theentity on objects in an environment, each of said objects including atleast one discernable characteristic; the environment is a computergenerated simulated environment; and the computer generated entityidentifies subsequently encountered objects by comparing discernedcharacteristics of the subsequently encountered objects with at leastone percept, each percept identifying discernable characteristics of arespective one of said objects.
 2. The computer generated entity ofclaim 1, wherein: simulated death occurs when the actions result in areduction of vitality below a preset level; and the computer generatedentity chooses such actions so as to avoid the simulated death.
 3. Thecomputer generated entity of claim 1, wherein: vitality level isdetermined by a quantity of energy packets.
 4. A computer interface,comprising: a digital life form having a plurality of attributes,including at least one attribute indicative of the vitality of thedigital life form; a plurality of actions which may be accomplished bythe digital life form; and a selection criteria for selecting from saidplurality of actions; and wherein repeated selection of actions which donot contribute to the vitality of the digital life form will result inthe simulated death of the digital life form; said digital life formperceives a plurality of objects in an environment; said objects areidentified by the digital life form according to percepts, each perceptidentifying perceivable characteristics of a respective one of saidobjects; said actions are primarily selected to keep the digital lifeform alive.
 5. The computer interface of claim 4, wherein: said actionsare taken to optimize at least one of a plurality of simulated feelings.6. The computer interface of claim 5, wherein: at least one of thesimulated feelings is a feeling of fullness.
 7. The computer interfaceof claim 6, wherein: the feeling of fullness is represented by aquantity of energy packets.
 8. A computer program product comprising acomputer usable medium having a computer readable program code embodiedthereon configured to operate on a computer, comprising: code to causethe computer to keep track of a list of attributes of a digital lifeform; code for causing the digital life form to perceive objects andperceivable characteristics of said objects; code for causing thedigital life form to formulate concepts based upon perceivedcharacteristics of at least one object; and code to cause the computerto cause the digital life form to take actions based upon the concepts.9. The computer program product of claim 8, wherein: at least some ofthe concepts are defined by a human tutor.
 10. The computer programproduct of claim 8, wherein: said actions are selected from a list ofactions programmed into the computer.
 11. The computer program productof claim 8, wherein: at least one consequence of the selection of saidactions is the termination of the digital life form.
 12. The computerprogram product of claim 8, wherein: at least one of the attributes ofthe digital life form is a simulated feeling.
 13. A method for creatinga digital life form, comprising: defining a digital life form; providingaccess for the digital life form to an environment; defining a pluralityof potential actions for the digital life form; providing objects in theenvironment, the objects including perceivable characteristics;providing the digital life form with the ability to form percepts basedon the perceivable characteristics of objects; and providing the digitallife form with the ability to select from said plurality of potentialactions based, at least in part, on the percepts.
 14. The method ofclaim 13, further comprising: providing consequences to the digital lifeform for such actions; and wherein the digital life form selects fromsaid plurality of potential actions in order to avoid certain of theconsequences.
 15. The method of claim 14, wherein: said digital lifeform includes a plurality of attributes.
 16. The method of claim 14,wherein: said environment is a computer generated simulated environment.17. The method of claim 14, wherein: at least one of said actionsincludes EAT.
 18. The method of claim 17, wherein: EAT is defined asassimilating energy packets to increase the vitality of said digitallife form.
 19. The method of claim 14, wherein: at least oneconsequences of said actions is the simulated death of said digital lifeform.
 20. The method of claim 13, and further including: providing astrategy for selecting from said plurality of actions.
 21. A method forforming concepts in a Digital Life Form, comprising: forming perceptsbased on perceived characteristics of objects; and using said perceivedcharacteristics to form concepts.
 22. The method of claim 21, wherein:concepts are compared to form other concepts in hierarchical conceptualchains.
 23. The method of claim 21, wherein: concepts are associatedwith natural language words.
 24. The method of claim 23, wherein:natural language words are associated into phrases, the meaning of whichis the union of the concepts and one or more conceptual chains thatultimately connect all the concepts in said conceptual chains to theassociated percepts.